Explore the vast range of titles available.

Son yıllarda, eğitim bilimlerindeki birincil çalışmaların sayısı artıkça kapsamlı ve sistematik araştırma sentezlerine olan ihtiyaç da artmaktadır. En etkili araştırma sentezi yollarından bir tanesi olan meta-analizin çeşitli uygulamalarının, sosyal bilimler ve eğitim bilimleri de dâhil olmak üzere birçok alanda teşvik edilmesinin temel sebebi budur. Bu makalenin temel amacı, meta-analizin diğer araştırma sentezi yöntemlerine kıyasla zayıf ve güçlü taraflarını sorgulayarak meta-analiz için kavramsal bir çerçeve oluşturmaktır. Bununla birlikte, sabit-etki ve rastgele-etkiler modellerinin karşılaştırılması, farklı etki büyüklüğü ölçüleri, analiz birimi, yayın yanlılığı ve birincil çalışmaların kalitesi gibi geçerlikle ilgili sorunlar ile heterojenlik, ara-değişken ve güç analizleri gibi bazı metodolojik ve istatistiksel hususlar detaylı şekilde tartışılmaktadır. Ayrıca bu makale kapsamında, meta-analizde kullanılan istatistiksel analizler için kullanılabilecek yazılımlar hakkında kısa ve

This paper considers a panel data model for predicting a binary outcome. The conditional probability of a positive response is obtained by evaluating a given distribution function (F) at a linear combination of the predictor variables. One of the predictor variables is unobserved. It is a random effect that varies across individuals but is constant over time. The semiparametric aspect is that the conditional distribution of the random effect, given the predictor variables, is unrestricted. This paper has two results. If the support of the observed predictor variables is bounded, then identification is possible only in the logistic case. Even if the support is unbounded, so that (from Manski (1987)) identification holds quite generally, the information bound is zero unless F is logistic. Hence consistent estimation at the standard pn rate is possible only in the logistic case.

I join two methodologies by illustrating the application of multilevel modeling principles to hazard-rate models with an emphasis on procedures for discrete-time data that contain repeatable events. I demonstrate this application using data taken from the 1995 National Survey of Family Growth (NSFG) to ascertain the relationship between multiple covariates and risk of subsequent marital dissolution. I consider both fixed- and random-effects versions of the multilevel model, as well as a Generalized Estimating Equation alternative to estimating random effects. I compare results obtained from the various estimators, noting why differences occur, and recommend when to choose the various alternatives. I also provide a set of SAS and STATA programs that can be used to analyze the NSFG data.

Assessing natural selection on a phenotypic trait in wild populations is of primary importance for evolutionary ecologists. To cope with the imperfect detection of individuals inherent to monitoring in the wild, we develop a nonparametric method for evaluating the form of natural selection on a quantitative trait using mark‐recapture data. Our approach uses penalized splines to achieve flexibility in exploring the form of natural selection by avoiding the need to specify an a priori parametric function. If needed, it can help in suggesting a new parametric model. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data for a wild population of sociable weavers (Philetairus socius) to investigate survival in relation to body mass. In agreement with previous parametric analyses, we found that lighter individuals showed a reduction in survival. However, the survival function was not symmetric, indicating that body mass might not be under stabilizing selection as suggested previously.

The pseudomarginal algorithm is a Metropolis–Hastings-type scheme which samples asymptotically from a target probability density when we can only estimate unbiasedly an unnormalized version of it. In a Bayesian context, it is a state of the art posterior simulation technique when the likelihood function is intractable but can be estimated unbiasedly by using Monte Carlo samples. However, for the performance of this scheme not to degrade as the number T of data points increases, it is typically necessary for the number N of Monte Carlo samples to be proportional to T to control the relative variance of the likelihood ratio estimator appearing in the acceptance probability of this algorithm. The correlated pseudomarginal method is a modification of the pseudomarginal method using a likelihood ratio estimator computed by using two correlated likelihood estimators. For random-effects models, we show under regularity conditions that the parameters of this scheme can be selected such that the relative variance of this likelihood ratio estimator is controlled when N increases sublinearly with T and we provide guidelines on how to optimize the algorithm on the basis of a non-standard weak convergence analysis. The efficiency of computations for Bayesian inference relative to the pseudomarginal method empirically increases with T and exceeds two orders of magnitude in some examples.

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.

ABSTRACT Undiagnosed hypertension (UHTN) remains a significant public health concern in Bangladesh, leading to severe complications due to delayed diagnosis and management. This systematic review and meta‐analysis examined the prevalence of UHTN among adults aged 18 years and older, using data from studies conducted in Bangladesh and published between 2010 and 2024. A comprehensive search of major databases yielded 1028 records, from which nine relevant studies, encompassing a total of 28949 participants, were selected and evaluated for quality using the Newcastle–Ottawa Scale, providing valuable insights into the prevalence of UHTN within the Bangladeshi population. The pooled prevalence of UHTN was 11% (95% CI: 6%–19%) based on a random‐effects model, with substantial heterogeneity (I2 = 99.5%, p < 0.0001). Subgroup analyses revealed higher prevalence in rural areas (13%; 95% CI: 4%–35%) compared to urban areas (12%; 95% CI: 1%–54%) and elevated occupational risk among bankers (17%; 95% CI: 0%–94%). While funnel plot asymmetry was noted, Egger's test (p = 0.3113) indicated no significant publication bias. Sensitivity analyses, including Leave‐One‐Out Analysis, affirmed the robustness of the pooled estimate. The findings underscore notable geographic, occupational, and sociodemographic disparities in UHTN prevalence, highlighting the need for nationwide screening programs and targeted community awareness campaigns, particularly in underserved rural areas. Further research is imperative to explore causal factors and inform effective prevention and management strategies.